Systems and methods for a mobile flight simulator of an electric aircraft

ABSTRACT

A system and method for a mobile flight simulator of an electric aircraft is illustrated. The first simulator housing instrument is configured to house a plurality of first flight simulator components. The second simulator housing instrument is configured to house a plurality of second flight simulator components. The pilot control is configured to receive a pilot command and transmit the pilot command to a computing device. The computing device is configured to communicatively connect each first flight simulator component of the plurality of flight simulator components and each second flight simulator component of the plurality of flight simulator components, generate a mobile flight simulation as a function of the pilot command, the mobile flight simulation including an electric aircraft model, and update the electric aircraft model as a function of the pilot command, the mobile flight simulation and a feedback datum.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircrafts. In particular, the present invention is directed to a systemand method for a mobile flight simulator of an electric aircraft.

BACKGROUND

In electric aircrafts (e.g. electric vertical take-off and landing(eVTOL) aircrafts), training of a pilot can be challenging due to thecomplexity of certification and/or license requirements. Integrationbetween the pilot training course content, flight simulator, andelectric aircraft can be highly complex due to the unique needs toachieve the ability to pilot an eVTOL aircraft. Flight simulators mayalso be made with heavy, immovable equipment; mobile simulators allowsthe simulator to be moved wherever the pilot/user is.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for a mobile flight simulator of an electricaircraft is illustrated. The system includes a first simulator housinginstrument, a second simulator housing instrument, a pilot control, anda computing device. The first simulator housing instrument is configuredto house a plurality of first flight simulator components. The secondsimulator housing instrument is configured to house a plurality ofsecond flight simulator components. The pilot control is configured toreceive a pilot command and transmit the pilot command to a computingdevice. The computing device is configured to communicatively connecteach first flight simulator component of the plurality of flightsimulator components and each second flight simulator component of theplurality of flight simulator components, receive the pilot command fromthe pilot control, generate a mobile flight simulation as a function ofthe pilot command, the mobile flight simulation including an electricaircraft model, and update the electric aircraft model as a function ofthe pilot command, the mobile flight simulation and a feedback datum.The electric aircraft model is configured to simulate a performance ofan electric aircraft as a function of the pilot command and provide afeedback datum to the plurality of first flight simulator components andthe plurality of second flight simulator components as a function of theperformance of the electric aircraft.

In another aspect, a method for a mobile flight simulator of an electricaircraft is illustrated. The method comprises housing, at a firstsimulator housing instrument, a plurality of first flight simulatorcomponents, housing, at a second simulator housing instrument, aplurality of second flight simulator components, coupling a pilotcontrol to the electric aircraft, receiving, at the pilot control, apilot command, transmitting, at the pilot control, the pilot command toa computing device, coupling a computing device to the electricaircraft, communicatively connecting, at the computing device, eachfirst flight simulator component of the plurality of flight simulatorcomponents and each second flight simulator component of the pluralityof flight simulator components, receiving, at the computing device, thepilot command from the pilot control, generating, at the computingdevice, a mobile flight simulation as a function of the pilot command,the mobile flight simulation including an electric aircraft model,simulating, at the electric aircraft model, a performance of an electricaircraft as a function of the pilot command, providing, at the electricaircraft model, a feedback datum to the plurality of first flightsimulator components and the plurality of second flight simulatorcomponents as a function of the performance of the electric aircraft,and updating, at the computing device, the electric aircraft model as afunction of the pilot command, the mobile flight simulation and thefeedback datum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of anelectric aircraft;

FIG. 2 is a block diagram of an exemplary embodiment of a system for amobile flight simulator of an electric aircraft;

FIG. 3 is a diagrammatic representation of an exemplary flightsimulator;

FIG. 4 is an exemplary embodiment of a microdome flight simulator;

FIG. 5 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 6 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 7 is a flow diagram of an exemplary embodiment of a method for amobile flight simulator of an electric aircraft; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto the invention as oriented in FIG. 1 . Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments of the inventive concepts defined in the appended claims.Hence, specific dimensions and other physical characteristics relatingto the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed to anaircraft with a monitoring system. In an embodiment, this disclosureincludes an aircraft configured to include a fuselage and a plurality offlight components attached to the fuselage. Aspects of the presentdisclosure include battery submodules connected to a power bus elementto provide charge to them depending on a health metric. Aspects of thepresent disclosure include at least a computing device to receive thehealth metric and decide whether the battery submodule needs to becharged. Exemplary embodiments illustrating aspects of the presentdisclosure are described below in the context of several specificexamples.

Referring now to FIG. 1 , an exemplary embodiment of an aircraft 100 isillustrated. In an embodiment, aircraft 100 is an electric aircraft. Asused in this disclosure an “aircraft” is any vehicle that may fly bygaining support from the air. As a non-limiting example, aircraft mayinclude airplanes, helicopters, commercial and/or recreationalaircrafts, instrument flight aircrafts, drones, electric aircrafts,airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets,airships, blimps, gliders, paramotors, and the like. Aircraft 100 mayinclude an electrically powered aircraft. In embodiments, electricallypowered aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft may include one or more mannedand/or unmanned aircrafts. Electric aircraft may include one or moreall-electric short takeoff and landing (eSTOL) aircrafts. For example,and without limitation, eSTOL aircrafts may accelerate plane to a flightspeed on takeoff and decelerate plane after landing. In an embodiment,and without limitation, electric aircraft may be configured with anelectric propulsion assembly. Electric propulsion assembly may includeany electric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/603,225, filed on Dec. 4, 2019, and entitled “ANINTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which isincorporated herein by reference.

Still referring to FIG. 1 , aircraft 100, may include a fuselage 104, aflight component 108 (or one or more flight components 108), computingdevice 112, and a sensor 116. Both the computing device 112 and sensor116 are described further herein with reference to FIG. 2 .

As used in this disclosure, a vertical take-off and landing (VTOL)aircraft is an aircraft that can hover, take off, and land vertically.An eVTOL, as used in this disclosure, is an electrically poweredaircraft typically using an energy source, of a plurality of energysources to power aircraft. To optimize the power and energy necessary topropel aircraft 100, eVTOL may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane style landing, and/or anycombination thereof. Rotor-based flight, as described herein, is wherethe aircraft generates lift and propulsion by way of one or more poweredrotors or blades coupled with an engine, such as a “quad-copter,”multi-rotor helicopter, or other vehicle that maintains its liftprimarily using downward thrusting propulsors. “Fixed-wing flight”, asdescribed herein, is where the aircraft is capable of flight using wingsand/or foils that generate lift caused by the aircraft's forwardairspeed and the shape of the wings and/or foils, such as airplane-styleflight.

Still referring to FIG. 1 , as used in this disclosure a “fuselage” is amain body of an aircraft, or in other words, the entirety of theaircraft except for a cockpit, nose, wings, empennage, nacelles, any andall control surfaces, and generally contains an aircraft's payload.Fuselage 104 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 104. Fuselage 104 may include a trussstructure. A truss structure may be used with a lightweight aircraft andincludes welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively include wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements mayinclude steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may include a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber, the latter of which will beaddressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 1 , aircraftfuselage 104 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that may include a long, thin, and rigidstrip of metal or wood that is mechanically coupled to and spans adistance from, station frame to station frame to create an internalskeleton on which to mechanically couple aircraft skin. A former (orstation frame) may include a rigid structural element that is disposedalong a length of an interior of aircraft fuselage 104 orthogonal to alongitudinal (nose to tail) axis of the aircraft and may form a generalshape of fuselage 104. A former may include differing cross-sectionalshapes at differing locations along fuselage 104, as the former is thestructural element that informs the overall shape of a fuselage 104curvature. In embodiments, aircraft skin may be anchored to formers andstrings such that the outer mold line of a volume encapsulated byformers and stringers includes the same shape as aircraft 100 wheninstalled. In other words, former(s) may form a fuselage's ribs, and thestringers may form the interstitials between such ribs. The spiralorientation of stringers about formers may provide uniform robustness atany point on an aircraft fuselage such that if a portion sustainsdamage, another portion may remain largely unaffected. Aircraft skin maybe attached to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 1 , fuselage 104 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may include aircraft skin made from plywoodlayered in varying grain directions, epoxy-impregnated fiberglass,carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 1 , fuselage 104may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 104 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 104 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofcomponents like screws, nails, dowels, pins, anchors, adhesives likeglue or epoxy, or bolts and nuts, to name a few. A subset of fuselageunder the umbrella of semi-monocoque construction includes unibodyvehicles. Unibody, which is short for “unitized body” or alternatively“unitary construction”, vehicles are characterized by a construction inwhich the body, floor plan, and chassis form a single structure. In theaircraft world, unibody may be characterized by internal structuralelements like formers and stringers being constructed in one piece,integral to the aircraft skin as well as any floor construction like adeck.

Still referring to FIG. 1 , stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may include aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 1 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 104. Monocoque includes only structuralskin, and in that sense, aircraft skin undergoes stress by appliedaerodynamic fluids imparted by the fluid. Stress as used in continuummechanics may be described in pound-force per square inch (lbf/in²) orPascals (Pa). In semi-monocoque construction stressed skin may bear partof aerodynamic loads and additionally may impart force on an underlyingstructure of stringers and formers.

Still referring to FIG. 1 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 104 may be configurablebased on the needs of the eVTOL per specific mission or objective. Thegeneral arrangement of components, structural elements, and hardwareassociated with storing and/or moving a payload may be added or removedfrom fuselage 104 as needed, whether it is stowed manually, automatedly,or removed by personnel altogether. Fuselage 104 may be configurable fora plurality of storage options. Bulkheads and dividers may be installedand uninstalled as needed, as well as longitudinal dividers wherenecessary. Bulkheads and dividers may be installed using integratedslots and hooks, tabs, boss and channel, or hardware like bolts, nuts,screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 104may also be configurable to accept certain specific cargo containers, ora receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 1 , aircraft 100 may include a plurality oflaterally extending elements attached to fuselage 104. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich may include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may include a plurality of geometries in planform view,swept swing, tapered, variable wing, triangular, oblong, elliptical,square, among others. A wing's cross section geometry may include anairfoil. An “airfoil” as used in this disclosure is a shape specificallydesigned such that a fluid flowing above and below it exert differinglevels of pressure against the top and bottom surface. In embodiments,the bottom surface of an aircraft can be configured to generate agreater pressure than does the top, resulting in lift. Laterallyextending element may include differing and/or similar cross-sectionalgeometries over its cord length or the length from wing tip to wherewing meets aircraft's body. One or more wings may be symmetrical aboutaircraft's longitudinal plane, which includes the longitudinal or rollaxis reaching down the center of aircraft through the nose andempennage, and plane's yaw axis. Laterally extending element may includecontrols surfaces configured to be commanded by a pilot or pilots tochange a wing's geometry and therefore its interaction with a fluidmedium, like air. Control surfaces may include flaps, ailerons, tabs,spoilers, and slats, among others. The control surfaces may dispose onthe wings in a plurality of locations and arrangements and inembodiments may be disposed at the leading and trailing edges of thewings, and may be configured to deflect up, down, forward, aft, or acombination thereof. An aircraft, including a dual-mode aircraft mayinclude a combination of control surfaces to perform maneuvers whileflying or on ground.

Still referring to FIG. 1 , aircraft 100 may include a plurality offlight components 108. As used in this disclosure a “flight component”is a component that promotes flight and guidance of an aircraft. In anembodiment, flight component 108 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling mayinclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude at least a lift propulsor. As used in this disclosure a“propulsor” is a component and/or device used to propel a craft upwardby exerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. Propulsor may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, propulsor may include arotor, propeller, paddle wheel and the like thereof. In an embodiment,propulsor may include a plurality of blades. As used in this disclosurea “blade” is a propeller that converts rotary motion from an engine orother power source into a swirling slipstream. In an embodiment, blademay convert rotary motion to push the propeller forwards or backwards.In an embodiment propulsor may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. The lift propulsor isfurther described herein with reference to FIG. 2 .

In an embodiment, and still referring to FIG. 1 , plurality of flightcomponents 108 may include one or more power sources. As used in thisdisclosure a “power source” is a source that that drives and/or controlsany other flight component. For example, and without limitation powersource may include a motor that operates to move one or more liftpropulsor components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking. Inan embodiment, power source may include an inverter. As used in thisdisclosure an “inverter” is a device that changes one or more currentsof a system. For example, and without limitation, inverter may includeone or more electronic devices that change direct current to alternatingcurrent. As a further non-limiting example, inverter may includereceiving a first input voltage and outputting a second voltage, whereinthe second voltage is different from the first voltage. In anembodiment, and without limitation, inverter may output a waveform,wherein a waveform may include a square wave, sine wave, modified sinewave, near sine wave, and the like thereof.

Still referring to FIG. 1 , power source may include an energy source.An energy source may include, for example, a generator, a photovoltaicdevice, a fuel cell such as a hydrogen fuel cell, direct methanol fuelcell, and/or solid oxide fuel cell, an electric energy storage device(e.g. a capacitor, an inductor, and/or a battery). An energy source mayalso include a battery cell, or a plurality of battery cells connectedin series into a module and each module connected in series or inparallel with other modules. Configuration of an energy sourcecontaining connected modules may be designed to meet an energy or powerrequirement and may be designed to fit within a designated footprint inan electric aircraft in which aircraft 100 may be incorporated.

In an embodiment, and still referring to FIG. 1 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, the energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, the energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, the energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein the energy source may have high powerdensity where the electrical power an energy source can usefully produceper unit of volume and/or mass is relatively high. The electrical poweris defined as the rate of electrical energy per unit time. An energysource may include a device for which power that may be produced perunit of volume and/or mass has been optimized, at the expense of themaximal total specific energy density or power capacity, during design.Non-limiting examples of items that may be used as at least an energysource may include batteries used for starting applications including Liion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 1 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Themodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducethe overall power output as the voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. The overall energy andpower outputs of at least an energy source may be based on theindividual battery cell performance or an extrapolation based on themeasurement of at least an electrical parameter. In an embodiment wherethe energy source includes a plurality of battery cells, the overallpower output capacity may be dependent on the electrical parameters ofeach individual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to the weakest cell. The energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude a pusher component. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher componentmay be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1145 N to force aircraft to in a horizontaldirection along the longitudinal axis. As a further non-limitingexample, pusher component may twist and/or rotate to pull air behind itand, at the same time, push aircraft 100 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which aircraft 100 ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 100 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 108 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Now referring to FIG. 2 , system 200 illustrates a block diagram of anexemplary embodiment of a system for a mobile flight simulator of anelectric aircraft. System 200 includes first simulator housinginstrument 204, second simulator housing instrument 208, pilot control212, pilot command 216, computing device 220, mobile flight simulation224, electric aircraft model 228, and feedback datum 232. In thisdisclosure, a “flight simulator” is a device that artificiallyre-creates aircraft flight and the environment in which it flies.Simulator module may include actual aircraft components that have beenseparated from a functioning aircraft or otherwise de-activated. Asimulator module may include a model or replica. In some cases,simulator module may include a physical twin of at least an aircraftcomponent. In some cases, simulator module may include a physicalcockpit. Furthermore, in this disclosure, “mobile” means the flightsimulator is relayed through mobile phones, handheld computers, tosimilarly small technology devices. Flight simulator may include anaugmented reality device. Flight simulator may include a plurality offlight simulator components.

Still referring to FIG. 2 , system 200 includes a first simulatorhousing instrument 204 that houses a plurality of first flight simulatorcomponents. In this disclosure, a first “simulator housing instrument”is the first component in which the flight simulator is located in.Housed inside of the first simulator housing instrument is a pluralityof first flight simulator components. In this disclosure, a “pluralityof first flight simulator components” are the first group of pieces oftechnology that include the flight simulator. Flight simulator mayinclude a plurality of flight simulator components that may include aseat, avionics controls, dashboard, the plurality of displays, inceptorstick, pedals, user device, a part replicating a fuselage, etc. Adjacentdisplays may be aligned next to each other in any order or formation. Ina non-limiting embodiment, adjacent displays may include at least aprojector. Adjacent displays may include a primary flight displayconfigured to be at the center of the alignment of displays. Adjacentdisplays may be collapsible, modular, foldable, separable, etc. Flightsimulator components may be modular, interchangeable, separable,collapsible, etc. The first simulator housing instrument may include anycontainer used for storage, transportation, delivery, shipping, and thelike. In a non-limiting embodiment, housing instrument may include ashipping container which may include a military grade shippingcontainer. First group of modular flight simulator components mayinclude a plurality of adjacent displays folded, a displays folded ontoa flight simulator dashboard, etc. First group of modular flightsimulator components may be configured to easily connect, assemble,integrate, etc, with a second group of modular flight simulatorcomponents.

Still referring to FIG. 2 , system 200 includes a second simulatorhousing instrument 208 which houses a plurality of second flightsimulator components. In this disclosure, a second “simulator housinginstrument” is the second component in which the flight simulator islocated in. Housed inside of the second simulator housing instrument isa plurality of second flight simulator components. In this disclosure, a“plurality of second flight simulator components” are the second groupof pieces of technology that include the flight simulator. Flightsimulator may include a plurality of flight simulator components thatmay include a seat, avionics controls, dashboard, the plurality ofdisplays, inceptor stick, pedals, user device, a part replicating afuselage, etc. Adjacent displays may be aligned next to each other inany order or formation. In a non-limiting embodiment, adjacent displaysmay include 4 projectors. Adjacent displays may include a primary flightdisplay configured to be at the center of the alignment of displays.Adjacent displays may be collapsible, modular, foldable, separable, etc.Flight simulator components may be modular, interchangeable, separable,collapsible, etc.

Still referring to FIG. 2 , system 200 includes a pilot control 212. Asused in this disclosure, a “pilot control” is a mechanism or means whichallows a pilot to control operation of flight components of an aircraft.For example, and without limitation, pilot control 212 may include acollective, inceptor, foot bake, steering and/or control wheel, controlstick, pedals, throttle levers, and the like. Pilot control 212 isconfigured to translate a pilot's desired torque for each flightcomponent of plurality of flight simulator components, such as andwithout limitation, a pusher component and a lift component. Pilotcontrol 212 is configured to control, via inputs and/or signals such asfrom a pilot, the pitch, roll, and yaw of aircraft. Pilot control 212may include a throttle lever, inceptor stick, collective pitch control,steering wheel, brake pedals, pedal controls, toggles, joystick, and thelike. One of ordinary skill in the art, upon reading the entirety ofthis disclosure would appreciate the variety of pilot input controlsthat may be present in an electric aircraft consistent with the presentdisclosure. Inceptor stick may be consistent with disclosure of inceptorstick in U.S. patent application Ser. No. 17/001,845, filed Aug. 25,2020, and titled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODEAIRCRAFT”, Attorney Docket No. 1024-034USC1, which is incorporatedherein by reference in its entirety. Collective pitch control may beconsistent with disclosure of collective pitch control in U.S. patentapplication Ser. No. 16/929,206, filed Jul. 15, 2020, and titled “HOVERAND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, Attorney Docket No.1024-034USU1, which is incorporated herein by reference in its entirety.Pilot control 212 may also include any of the pilot controls asdisclosed in U.S. patent application Ser. No. 17/218,387, filed Mar. 31,2021, and entitled “METHOD AND SYSTEM FOR FLY-BY-WIRE FLIGHT CONTROLCONFIGURED FOR USE IN ELECTRIC AIRCRAFT”, Attorney Docket No.1024-082USU1. Pilot control 212 may be physically located in the cockpitof aircraft or remotely located outside of the aircraft in anotherlocation communicatively connected to at least a portion of theaircraft. Pilot control 212 may include buttons, switches, or otherbinary inputs in addition to, or alternatively than digital controlsabout which a plurality of inputs may be received. Pilot control 212 maybe configured to receive a physical manipulation of a control like apilot using a hand and arm to push or pull a lever, or a pilot using afinger to manipulate a switch. Pilot control 212 may also be operated bya voice command by a pilot to a microphone and computing systemconsistent with the entirety of this disclosure. Pilot control 212 maybe communicatively connected to any other component presented in system,the communicative connection may include redundant connectionsconfigured to safeguard against single-point failure. Pilot control 212may be incorporated in a simulator located remotely from aircraft.Additionally, pilot control 212 may be overridden by autopilot as analternate embodiment. In this disclosure, “autopilot” may refer to adevice or mechanism that keeps an aircraft on a set course withoutintervention of the pilot; it also denotes any portion of the flightcontroller that performs autonomous flight.

Referring still to FIG. 2 , pilot control 212 is configured to receiveand transmit a pilot command 216 to a computing device 220. In thisdisclosure, “pilot command” is an element of data identifying and/ordescribing the desire of the pilot to follow a flight path. Examples ofpilot command 216 include but are not limited to ascent of the aircraftafter takeoff, descent of the aircraft during landing, and the like,among others. Pilot command 216 may be manually entered by pilot and/ormay be obtained from autopilot, or the like. Additionally but notlimited to, pilot command 216 may be obtained based on visual cues,tactile cues, flight display, and the like. Pilot command 216 may alsobe obtained from a pilot who may be located in a simulator controllingaircraft 100 remotely. Pilot command 216 may be generated by the inputof the pilot control. In some embodiments, pilot command 216 may beconfigured to identify a torque of a flight component of an electricaircraft model as a function of the input of the user. Pilot command 216may be configured to identify a torque applied to a flight component ofthe electric aircraft model based on the user input of the computingdevice.

Still referring to FIG. 2 , system 200 includes a computing device 220.In this disclosure, a “computing device” is any electronic equipmentcontrolled by a CPU and may include a computer, tablet, smartphone, orthe like. Computing device 220 may include a flight controller. As usedin this disclosure a “flight controller” is a computing device of aplurality of computing devices dedicated to data storage, security,distribution of traffic for load balancing, and flight instruction.Flight controller may include and/or communicate with any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Further, flightcontroller may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. In embodiments, flight controller may beinstalled in an aircraft, may control the aircraft remotely, and/or mayinclude an element installed in the aircraft and a remote element incommunication therewith. In an embodiment, and without limitation,flight controller may be configured to command a plurality of flightcomponents. Computing device 220 is described further herein withreference to FIG. 7 .

Still referring to FIG. 2 , computing device 220 is configured tocommunicatively connect each first flight simulator component of theplurality of flight simulator components and each second flightsimulator component of the plurality of flight simulator components.Computing device 220 may be a remote device configured to connect eachmodular flight simulator component. Computing device 220 may beconfigured to identify each modular flight simulator component to thefirst housing instrument 204 or second housing instrument 208. Computingdevice 220 may be configured to disconnect electronically the componentsof one group with the components of another group to allow for fasterelectronic connecting or reassembling of the components as a whole.

Still referring to FIG. 2 , computing device 220 is also configured togenerate a mobile flight simulation 224 as a function of the pilotcommand. Computing device 220 may be configured to generate a simulation224. For example, and without limitation, computing device 220 mayinclude one or more devices capable of modeling, simulating, analyzing,and the like thereof a multidomain system. Computing device 220 may beconfigured to generate an electric aircraft model 228. As used in thisdisclosure a “model” is a representation and/or graphical image denotingan artificial and/or virtual aircraft in flight. In an embodiment, andwithout limitation, electric aircraft model 228 may denote anenvironment in which the artificial and/or virtual aircraft flies. Insome cases, electric aircraft model 228 may include one or more physicsmodels, which represent analytically or through data-based, such aswithout limitation machine-learning processes, one or more physicalphenomena. One or more physical phenomena may be associated with anaircraft and/or an environment. For example, some versions of electricaircraft model 228 may include thermal models representing aircraftcomponents by way of thermal modeling. Thermal modeling techniques may,in some cases, include analytical representation of one or more ofconvective hear transfer (for example by way of Newton's Law ofCooling), conductive heat transfer (for example by way of Fourierconduction), radiative heat transfer, and/or advective heat transfer. Insome cases, electric aircraft model 228 may include models representingfluid dynamics. For example, in some embodiments, simulation 224 mayinclude a representation of turbulence, wind shear, air density, cloud,precipitation, and the like. In some embodiments, electric aircraftmodel 228 may include at least a model representing optical phenomenon.For example, simulation 224 may include optical models representative oftransmission, reflectance, occlusion, absorption, attenuation, andscatter. Electric aircraft model 228 may include non-analytical modelingmethods; for example, simulation 224 may include, without limitation, aMonte Carlo model for simulating optical scatter within a turbid medium,for example clouds. In some embodiments, electric aircraft model 228 mayrepresent Newtonian physics, for example motion, pressures, forces,moments, and the like. Simulation 224 may include Microsoft FlightSimulator from Microsoft of Redmond, Wash., U.S.A. Additionally oralternatively, electric aircraft model 228 may include one or moreaerodynamics models, inertial models, mass models, propeller models,pusher motor models, Euler models, sensor models, battery models, andthe like thereof. In an embodiment, and without limitation, sensormodels may denote one or more representations of injecting noise, failedsensors, white noise potential, transfer functions, and the likethereof. In another embodiment, battery models may denote one or moreestimation algorithms, power capabilities, thermal outputs, powercapabilities, and the like thereof. In another embodiment, electricaircraft model 228 may include a simple path and/or a variant path. Asused in this disclosure a “simple path” is a less complex algorithm thatallows for a faster simulation. In an embodiment, and withoutlimitation, simple path may denote a fast simulation, wherein theenhanced speed reduces the accuracy of electric aircraft model 228. Asused in this disclosure a “variant path” is a more complex algorithmthat allows for a slower simulation. In an embodiment, and withoutlimitation, variant path may denote a slow simulation, wherein thereduced speed enhances the accuracy of electric aircraft model 228.

Referring still to FIG. 2 , mobile flight simulation 224 includes anelectric aircraft model 228. In some embodiments, simulation 224 may beconfigured to generate a flight component of an electric aircraft model228. A “model,” as used in this disclosure, is a data structure and/orprogram that can simulate one or more relevant aspects of an object ordevice such as a flight component; one or more relevant aspects mayinclude one or more behaviors affecting a designed use of the flightcomponent to aid in flying and/or navigation of an aircraft. As used inthis disclosure a “flight component” is a portion of an aircraft thatcan be moved or adjusted to affect one or more flight elements. Forexample, a flight component may include a component used to affect theaircrafts' roll and pitch which may include one or more ailerons. As afurther example, a flight component may include a rudder to control yawof an aircraft. In some embodiments, electric aircraft model 228 mayinclude a propulsor model. The propulsor model may include a set of datacorresponding to a virtual propulsor's torque output. The propulsormodel may include a computer program or computer application thatrepresents propulsor torque performance given a certain set ofconditions. This set of conditions may include a performance parameter.The performance parameters may include environmental parameters such asair density, air speed, true airspeed, relative airspeed, temperature,humidity level, and weather conditions, among others. The performanceparameter may include propulsor parameters that define a propulsorsphysical characteristics and/or specifications such as materialproperties, electrical characteristics, propulsor type, weight,geometry, speed, and revolutions per minute (rpm), among others. Theperformance parameter may include velocity and/or speed in a pluralityof ranges and direction such as vertical speed, horizontal speed,changes in angle or rates of change in angles like pitch rate, rollrate, yaw rate, or a combination thereof, among others.

In some embodiments, referring still to FIG. 2 , electric aircraft model228 may be configured to generate a model torque datum including a modeltorque datum threshold. A “model torque datum”, for the purposes of thisdisclosure, is an element of data that represents an ideal torque outputform an ideal propulsor model. One of ordinary skill in the art, afterreviewing the entirety of this disclosure, would appreciate that modeltorque datum is the torque output an ideal virtual torque data from aperfect propulsor given performance parameter of a plurality ofperformance parameters. For example, in a nonlimiting embodiment, thepropulsor model may include a performance parameter including airdensity, propulsor type, electrical input, and rpm. The model torquedatum may be generated by to represent what a perfect (ideal) propulsorwould output as torque given the performance parameters. The modeltorque datum threshold may include a range of acceptable torque valuesassociated with the model torque datum. The model torque datum thresholdmay include a minimum and maximum torque value associated with the modeltorque datum. Simulation 224 may be configured to detect if the outputtorque datum is outside the model torque datum threshold, which may thentrigger detection of datums consistent with this disclosure. In someembodiments, electric aircraft model 228 may be configured to generatemodels of aircrafts and flight components as described in U.S. patentapplication Ser. No. 17/348,916 filed Jun. 16, 2021, titled “METHODS ANDSYSTEMS FOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF ANDLANDING (EVTOL) AIRCRAFT”, of which is incorporated by reference hereinin its entirety.

With continued reference to FIG. 2 , electric aircraft model may beconfigured to include a torque percentage datum. A “torque percentagedatum”, for the purposes of this disclosure, is an element of datarepresenting the actual torque produced by at least a propulsor comparedto the modeled torque output of the same ideal propulsor given the sameperformance parameters. For example, in a nonlimiting embodiment,electric aircraft model 228 may generate a torque percentage datum bydividing an output torque datum by the model torque datum. The torquepercentage datum may represent the torque output of an actual propulsorversus the same propulsor in an ideal world, giving way to a percentageof ideal torque. The torque percentage datum may be represented as afraction, percentage, decimal, or other mathematical representation ofpart of a whole. One of ordinary skill in the art, after reviewing theentirety of this disclosure would appreciate that there are virtuallylimitless visual, auditory, haptic or other types of representationsthat the torque percentage datum may take.

In some embodiments and still referring to FIG. 2 , electric aircraftmodel 228 may include a battery model. The battery model may include anymodel related to at least property, characteristic, or function of abattery located within aircraft. In some cases, the battery model mayinclude a model of a battery controller, management, and/or monitoringsystem. Disclosure related to battery management for eVTOL aircraft maybe found in patent application Ser. Nos. 17/108,798 and 17/111,002,entitled “PACK LEVEL BATTERY MANAGEMENT SYSTEM” and “ELECTRICALDISTRIBUTION MONITORING SYSTEM FOR AN ELECTRIC AIRCRAFT,” respectively,each of which is incorporated herein by reference in its entirety. Insome cases, a battery model may include an electrochemical model ofbattery, which may be predictive of energy efficiencies and heatgeneration and transfer of at least a battery. In some cases, a batterymodel may be configured to predict battery lifetime, given known batteryparameters, for example measured battery performance, temperature,utilization, and the like.

Still referring to FIG. 2 , electric aircraft model 228 may beconfigured to produce a simulation of at least a flight element of anelectric aircraft. As used in this disclosure a “simulation” is animitation of aircraft and/or flight of an aircraft. For example, andwithout limitation, simulation may denote at least a flight element ofan electric aircraft, wherein a flight element is an element of datumdenoting a relative status of aircraft. In an embodiment, and withoutlimitation, a flight element may denote one or more torques, thrusts,airspeed velocities, forces, altitudes, groundspeed velocities,directions during flight, directions facing, forces, orientations, andthe like thereof. In an embodiment, and without limitation, electricaircraft model 228 may produce a simulation denoting one or moreadjustments to an altitude as a function of an adjusted and/or shifteddirection during flight. As a further non-limiting example, electricaircraft model 228 may produce a simulation denoting one or moremodifications to an airspeed velocity as a function of a changing and/oraltered windspeed velocity. In an embodiment, and without limitation,electric aircraft model 228 may be configured to include operationaldata of a flight component for a plurality of simulated conditions. Asused in this disclosure “operational data” is information denoting oneor more operational functions of a flight component. For example, andwithout limitation, operational data may denote one or more rotationalspeeds, torques, forces, rpms, and the like thereof. For example, andwithout limitation, operational data may denote that a propulsor isrotating at a speed of 800 rpms. As a further non-limiting example,operational data may denote that an aileron is angled at 3.3° upward. Inan embodiment, and without limitation, operational data may denote oneor more voltage levels, electromotive force, current levels,temperature, current speed of rotation, and the like thereof. In anotherembodiment, operational data may denote one or more electricalparameters of a flight component such as a voltage, current, and/orohmic resistance of a flight component. As used in this disclosure a“simulated condition” is a condition and/or environment that is to besimulated for flight condition. For example, and without limitation,simulated conditions may include an environmental condition of a windforce and/or precipitation. As a further non-limiting example, simulatedconditions may include one or more alterations and/or modifications ofoperational datum. Simulation 224 may include algorithms and/or machinelearning models, systems, and any combination thereof found in theflight controller as described with reference to FIG. 6 below. In someembodiments, electric aircraft model 228 may include electric aircraftcomponents and structures as described in further detail throughout thisembodiment.

Still referring to FIG. 2 , electric aircraft model 228 is configured tosimulate a performance of an electric aircraft as a function of thepilot command. A “performance” as defined in this disclosure is thedifference of an action relative to a desired goal or outcome of theaction. Performance may be determined from a plurality of factors.Performance may be determined based on pilot command 216. In someembodiments, performance may be determined relative to model parameters.In some embodiments, performance may be determined based on a set ofgoals of a training course included in model parameters. Performance mayinclude information about electric aircraft model 228, such as healthand fuel supply. In some embodiments performance may be determined basedon a time score. In some embodiments, performance may be determinedbased on a flight path taken. In some embodiments, performance may bedetermined based on a deviation from a desired flight path. In someembodiments, performance may be determined based on fuel efficiency. Insome embodiments, performance may be determined based on a landing ofelectric aircraft model 228. The landing may be scored based on aplurality of metrics. The landing may be scored based on descent speed.The landing may be scored based on landing accuracy in a landing zone.The landing may be scored based on power efficiency. In someembodiments, computing device 220 may be configured to transformperformance into a feedback datum. Feedback datum 232 may be configuredto relay performance data to a user. In some embodiments, feedback datum232 may include a user score. In some embodiments, the user score may bedetermined by a plurality of factors. In some embodiments, feedbackdatum 232 may include a breakdown of areas of improvement based onperformance. The areas of improvement may include power efficiency,flight path deviation, electric aircraft health and/or other metrics. Insome embodiments, feedback datum 232 may be configured to be displayedon a GUI of pilot control 212. In some embodiments, feedback datum 232may be a real time feedback shown in pilot control 212. In someembodiments, feedback datum 232 may include suggestions for flightmaneuvers. In some embodiments, feedback datum 232 may include anaverage score from a history of simulated flights. In some embodiments,feedback datum 232 may be shown relative to performance of other users.In some embodiments, feedback datum 232 may be shown relative to a goalof a training course. In some embodiments, feedback datum 232 may beconfigured to display a battery performance metric. The batteryperformance metric may include, but is not limited to, battery charge,battery health, battery temperature, and/or battery usage. In someembodiments, feedback datum 232 may be configured to suggest a betterflight maneuver and/or path to preserve the battery of electric aircraftmodel 228. In some embodiments, feedback datum 232 may be configured totake control of pilot control 212 to illustrate a better way of pilotingan electric aircraft for a user. In some embodiments, feedback datum 232may be an auditory stimulus. In some embodiments, the auditory stimulusmay include alerts. The alerts may include, but are not limited to,altitude alerts, battery alerts, temperature alerts, speed alerts,propulsion system alerts, collision alerts, or other alerts, alone or incombination. In some embodiments, computing device 220 may be configuredto send performance and feedback datum 232 to an external computingdevice. In some embodiments, computing device 220 may retain a historyof performance for a plurality of users in a database.

Still referring to FIG. 2 , electric aircraft model 228 is alsoconfigured to provide a feedback datum to the plurality of first flightsimulator components and the plurality of second flight simulatorcomponents as a function of the performance of the electric aircraft. Insome embodiments, the simulation may be configured to provide feedbackdatum 232 to a user. Feedback datum 232 may include feedback on flightcourse deviation, battery health and charge status, electric aircrafthealth status, and electric aircraft speed. In some embodiments, thefeedback may provide feedback found in a primary flight display.Feedback datum 232 is further described above.

Referring still to FIG. 2 , computing device 220 is further configuredto update the electric aircraft model as a function of the pilot command216, the mobile flight simulation 224 and the feedback datum 232.Simulation 224 may be configured to update a trajectory of electricaircraft model 228 based on the pilot command 216. Simulation 224 may beconfigured to update a movement of one or more flight components of anelectric aircraft based on pilot command 216. In some embodiments,simulation 224 may be configured to provide feedback datum 232 to auser. Feedback datum 232 may include feedback on flight coursedeviation, battery health and charge status, electric aircraft healthstatus, and electric aircraft speed. In some embodiments, the feedbackdatum 232 may provide feedback found in a primary flight display.

Now referring to FIG. 3 , a diagrammatic representation of an exemplaryflight simulator is illustrated and known as system 300. The flightsimulator illustrated here is categorized as a Thunderdome flightsimulator, which enables multiple crew training missions and allowsspectator to sit in the airframe; another type of flight simulator isexplained herein with reference to FIG. 4 . Mobile flight simulatorexplained in this disclosure may be any of the flight simulatorexplained here or in U.S. Nonprovisional application Ser. No.17/524,355, filed on Nov. 11, 2021, and entitled “SYSTEMS AND METHODSFOR SIMULATING AN ELECTRICAL VERTICAL TAKEOFF AND LANDING (EVTOL)AIRCRAFT,” the entirety of which is incorporated herein by reference.System 300 includes fuselage 304, flight components 308, cockpit 312,pilot seats 316, pilot 320, pilot inputs 324, concave screen 328,projectors 332, scaffolding 336, and computing device 340. Fuselage 304and flight components 308 may be any fuselage or flight componentsexplained herein with reference to FIG. 1 . Fuselage 304 has an entranceto cockpit 312 which is the compartment the pilot 320, pilot controls,and pilot seats 316 are housed in. Cockpit 312 includes pilot inputs 324which are configured to detect pilot datums. Pilot inputs 324 may be anygauge, throttle lever, clutch, dial, control, or any other mechanical orelectrical device that is configured to be manipulated by pilot 320 toreceive information. Flight simulator 300 also may include concavescreen 328 facing fuselage 304; projectors 332 are directed at concavescreen 328 and projectors 332 are each configured to project an imageonto concave screen 328. Projectors 332 may be attached to or otherwiseon fuselage 304 or projectors 332 may be attached to an independentsupportive structure such as scaffolding 336. Flight simulator 300 alsomay include computing device 340, which is further explained herein withreference to FIGS. 2 and 8 .

Now referring to FIG. 4 , an exemplary embodiment of a microdome flightsimulator is illustrated. Microdome flight simulator is similar to aThunderdome flight simulator, but does not include a fuselage, more thanone pilot, as many projectors, or autopilot. It is used for recruitment,development, and training, but not for certification like theThunderdome. Overall, the microdome is also smaller than theThunderdome. Mobile flight simulator as described in this disclosure maybe any of the flight controllers mentioned herein or with reference tothe application referenced above.

Now referring to FIG. 5 , an exemplary embodiment 500 of a possiblecomputing device 220 is illustrated. Thus, a flight controller isillustrated. As used in this disclosure a “flight controller” is acomputing device of a plurality of computing devices dedicated to datastorage, security, distribution of traffic for load balancing, andflight instruction. Flight controller may include and/or communicatewith any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Further, flight controller may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. In embodiments, flightcontroller may be installed in an aircraft, may control aircraft 100remotely, and/or may include an element installed in aircraft and aremote element in communication therewith.

In an embodiment, and still referring to FIG. 5 , flight controller mayinclude a signal transformation component 508. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 508 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component508 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 508 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 508 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 508 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 5 , signal transformation component 508 may beconfigured to optimize an intermediate representation 512. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 508 may optimize intermediate representation as a function ofa dataflow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 508 may optimizeintermediate representation 512 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 508 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 508 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller. For example, and without limitation, native machinelanguage may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformationcomponent 508 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 5 , flight controller mayinclude a reconfigurable hardware platform 516. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 516 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 5 , reconfigurable hardware platform 516 mayinclude a logic component 520. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 520 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 520 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 520 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 520 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 520 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 512. Logiccomponent 520 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller. Logiccomponent 520 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 520 may beconfigured to execute the instruction on intermediate representation 512and/or output language. For example, and without limitation, logiccomponent 520 may be configured to execute an addition operation onintermediate representation 512 and/or output language.

In an embodiment, and without limitation, logic component 520 may beconfigured to calculate a flight element 524. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 524 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 524 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 524 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 5 , flight controller may include a chipsetcomponent 528. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 528 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 520 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 528 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 520 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 528 maymanage data flow between logic component 520, memory cache, and a flightcomponent 108. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 108 may include acomponent used to affect the aircrafts' roll and pitch which may includeone or more ailerons. As a further example, flight component 108 mayinclude a rudder to control yaw of an aircraft. In an embodiment,chipset component 528 may be configured to communicate with a pluralityof flight components as a function of flight element 524. For example,and without limitation, chipset component 528 may transmit to anaircraft rotor to reduce torque of a first lift propulsor and increasethe forward thrust produced by a pusher component to perform a flightmaneuver.

In an embodiment, and still referring to FIG. 5 , flight controller isconfigured to produce both autonomous and semi-autonomous flight. Asused in this disclosure an “autonomous function” is a mode and/orfunction of flight controller that controls aircraft automatically. Forexample, and without limitation, autonomous function may perform one ormore aircraft maneuvers, take offs, landings, altitude adjustments,flight leveling adjustments, turns, climbs, and/or descents. As afurther non-limiting example, autonomous function may adjust one or moreairspeed velocities, thrusts, torques, and/or groundspeed velocities. Asa further non-limiting example, autonomous function may perform one ormore flight path corrections and/or flight path modifications as afunction of flight element 524. In an embodiment, autonomous functionmay include one or more modes of autonomy such as, but not limited to,autonomous mode, semi-autonomous mode, and/or non-autonomous mode. Asused in this disclosure “autonomous mode” is a mode that automaticallyadjusts and/or controls aircraft and/or the maneuvers of aircraft in itsentirety. For example, autonomous mode may denote that flight controllerwill adjust the aircraft. As used in this disclosure a “semi-autonomousmode” is a mode that automatically adjusts and/or controls a portionand/or section of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller will control the ailerons and/orrudders. As used in this disclosure “non-autonomous mode” is a mode thatdenotes a pilot will control aircraft and/or maneuvers of aircraft inits entirety.

In an embodiment, and still referring to FIG. 5 , flight controller maygenerate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 524 and a pilot signal536 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 536may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 536 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 536may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 536 may include an explicitsignal directing flight controller to control and/or maintain a portionof aircraft, a portion of the flight plan, the entire aircraft, and/orthe entire flight plan. As a further non-limiting example, pilot signal536 may include an implicit signal, wherein flight controller detects alack of control such as by a malfunction, torque alteration, flight pathdeviation, and the like thereof. In an embodiment, and withoutlimitation, pilot signal 536 may include one or more explicit signals toreduce torque, and/or one or more implicit signals that torque may bereduced due to reduction of airspeed velocity. In an embodiment, andwithout limitation, pilot signal 536 may include one or more localand/or global signals. For example, and without limitation, pilot signal536 may include a local signal that is transmitted by a pilot and/orcrew member. As a further non-limiting example, pilot signal 536 mayinclude a global signal that is transmitted by air traffic controland/or one or more remote users that are in communication with the pilotof aircraft. In an embodiment, pilot signal 536 may be received as afunction of a tri-state bus and/or multiplexor that denotes an explicitpilot signal should be transmitted prior to any implicit or global pilotsignal.

Still referring to FIG. 5 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure, “remotedevice” is an external device to flight controller. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elastic net regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 5 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 5 , flight controller may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller. Remote device and/or FPGAmay transmit a signal, bit, datum, or parameter to flight controllerthat at least relates to autonomous function. Additionally oralternatively, the remote device and/or FPGA may provide an updatedmachine-learning model. For example, and without limitation, an updatedmachine-learning model may be included of a firmware update, a softwareupdate, an autonomous machine-learning process correction, and the likethereof. As a non-limiting example a software update may incorporate anew simulation data that relates to a modified flight element.Additionally or alternatively, the updated machine learning model may betransmitted to the remote device and/or FPGA, wherein the remote deviceand/or FPGA may replace the autonomous machine-learning model with theupdated machine-learning model and generate the autonomous function as afunction of the flight element, pilot signal, and/or simulation datausing the updated machine-learning model. The updated machine-learningmodel may be transmitted by the remote device and/or FPGA and receivedby flight controller as a software update, firmware update, or correctedautonomous machine-learning model. For example, and without limitationautonomous machine learning model may utilize a neural netmachine-learning process, wherein the updated machine-learning model mayincorporate a gradient boosting machine-learning process.

Still referring to FIG. 5 , flight controller may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. Further, flight controller may communicate with one ormore additional devices as described below in further detail via anetwork interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 5 , flight controller mayinclude, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controllermay include one or more flight controllers dedicated to data storage,security, distribution of traffic for load balancing, and the like.Flight controller may be configured to distribute one or more computingtasks as described below across a plurality of flight controllers, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices. Forexample, and without limitation, flight controller may implement acontrol algorithm to distribute and/or command the plurality of flightcontrollers. As used in this disclosure a “control algorithm” is afinite sequence of well-defined computer implementable instructions thatmay determine the flight component of the plurality of flight componentsto be adjusted. For example, and without limitation, control algorithmmay include one or more algorithms that reduce and/or prevent aviationasymmetry. As a further non-limiting example, control algorithms mayinclude one or more models generated as a function of a softwareincluding, but not limited to Simulink by MathWorks, Natick, Mass., USA.In an embodiment, and without limitation, control algorithm may beconfigured to generate an auto-code, wherein an “auto-code,” is usedherein, is a code and/or algorithm that is generated as a function ofthe one or more models and/or software's. In another embodiment, controlalgorithm may be configured to produce a segmented control algorithm. Asused in this disclosure a “segmented control algorithm” is controlalgorithm that has been separated and/or parsed into discrete sections.For example, and without limitation, segmented control algorithm mayparse control algorithm into two or more segments, wherein each segmentof control algorithm may be performed by one or more flight controllersoperating on distinct flight components.

In an embodiment, and still referring to FIG. 5 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 108. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furtherincludes separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 5 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 512 and/or output language from logiccomponent 520, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 5 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 5 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 5 , flight controller may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 5 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 5 , flight controller may include asub-controller 540. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller may be and/or include adistributed flight controller made up of one or more sub-controllers.For example, and without limitation, sub-controller 540 may include anycontrollers and/or components thereof that are similar to distributedflight controller and/or flight controller as described above.Sub-controller 540 may include any component of any flight controller asdescribed above. Sub-controller 540 may be implemented in any mannersuitable for implementation of a flight controller as described above.As a further non-limiting example, sub-controller 540 may include one ormore processors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data across the distributedflight controller as described above. As a further non-limiting example,sub-controller 540 may include a controller that receives a signal froma first flight controller and/or first distributed flight controllercomponent and transmits the signal to a plurality of additionalsub-controllers and/or flight components.

Still referring to FIG. 5 , flight controller may include aco-controller 544. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller as componentsand/or nodes of a distributer flight controller as described above. Forexample, and without limitation, co-controller 544 may include one ormore controllers and/or components that are similar to flightcontroller. As a further non-limiting example, co-controller 544 mayinclude any controller and/or component that joins flight controller todistributer flight controller. As a further non-limiting example,co-controller 544 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller to distributed flightcontrol system. Co-controller 544 may include any component of anyflight controller as described above. Co-controller 544 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 5 , flightcontroller may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller may be configured to perform a single stepor sequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Referring now to FIG. 6 , an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6 ,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 6 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 616 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 6 , machine-learning module 600 may beconfigured to perform a lazy-learning process 620 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 604. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 604 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 6 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6 , machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 604. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process628 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 6 , machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6 , machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 7 , an exemplary embodiment of method 700 for amobile flight simulator in an electric aircraft. The electric aircraftmay include, but without limitation, any of the aircraft as disclosedherein and described above with reference to at least FIG. 1 .

Still referring to FIG. 7 , at step 705, method 700 includes housing, ata first simulator housing instrument 204, a plurality of first flightsimulator components. First housing instruments 204 may include ashipping container. First group of flight simulator components includesa plurality of adjacent displays. Adjacent displays include at least aprojector. The first simulator housing instrument may include, butwithout limitation, any of the simulator housing instruments asdisclosed herein and described above with reference to at least FIG. 2 .

Still referring to FIG. 7 , at step 710, method 700 includes housing, ata second simulator housing instrument 208, a plurality of second flightsimulator components. The second simulator housing instrument mayinclude, but without limitation, any of the simulator housinginstruments as disclosed herein and described above with reference to atleast FIG. 2 .

Still referring to FIG. 7 , at step 715, method 700 includestransmitting, at the pilot control 212, the pilot command 216 to acomputing device 220. The pilot control may include, but withoutlimitation, any of the pilot controls as disclosed herein and describedabove with reference to at least FIG. 2 . The pilot command may include,but without limitation, any of the pilot commands as disclosed hereinand described above with reference to at least FIG. 2 . The computingdevice may include, but without limitation, any of the computing devicesas disclosed herein and described above with reference to at least FIGS.2-7 .

Still referring to FIG. 7 , at step 720, method 700 includescommunicatively connecting, at the computing device 220, each firstflight simulator component of the plurality of flight simulatorcomponents and each second flight simulator component of the pluralityof flight simulator components. The computing device may include, butwithout limitation, any of the computing devices as disclosed herein anddescribed above with reference to at least FIGS. 2-7 .

Still referring to FIG. 7 , at step 725, method 700 includes generating,at the computing device 220, a mobile flight simulation 224 as afunction of the pilot command 216, the mobile flight simulation 224including an electric aircraft model 228. Plurality of first and secondflight components display the electric aircraft model 228. Electricaircraft model 228 includes an eVTOL model. The computing device mayinclude, but without limitation, any of the computing devices asdisclosed herein and described above with reference to at least FIGS.2-7 . The mobile flight simulation may include, but without limitation,any of the mobile flight simulations as disclosed herein and describedabove with reference to at least FIG. 2 . The pilot command may include,but without limitation, any of the pilot commands as disclosed hereinand described above with reference to at least FIG. 2 . The electricaircraft model may include, but without limitation, any of the models asdisclosed herein and described above with reference to at least FIG. 2 .

Still referring to FIG. 7 , at step 730, method 700 includes simulating,at the electric aircraft model 228, a performance of an electricaircraft as a function of the pilot command 216. The electric aircraftmodel may include, but without limitation, any of the models asdisclosed herein and described above with reference to at least FIG. 2 .The pilot command may include, but without limitation, any of the pilotcommands as disclosed herein and described above with reference to atleast FIG. 2 .

Still referring to FIG. 7 , at step 735, method 700 includes providing,at the electric aircraft model 228, a feedback datum 232 to theplurality of first flight simulator components and the plurality ofsecond flight simulator components as a function of the performance ofthe electric aircraft. The electric aircraft model may include, butwithout limitation, any of the models as disclosed herein and describedabove with reference to at least FIG. 2 . The feedback datum mayinclude, but without limitation, any of the feedback datum as disclosedherein and described above with reference to at least FIG. 2 .

Still referring to FIG. 7 , at step 740, method 700 includes updating,at the computing device 220, the electric aircraft model 228 as afunction of the pilot command 216, the mobile flight simulation 224 andthe feedback datum 232. The computing device may include, but withoutlimitation, any of the computing devices as disclosed herein anddescribed above with reference to at least FIGS. 2-7 . The electricaircraft model may include, but without limitation, any of the models asdisclosed herein and described above with reference to at least FIG. 2 .The pilot command may include, but without limitation, any of the pilotcommands as disclosed herein and described above with reference to atleast FIG. 2 . The mobile flight simulation may include, but withoutlimitation, any of the mobile flight simulations as disclosed herein anddescribed above with reference to at least FIG. 2 . The feedback datummay include, but without limitation, any of the feedback datum asdisclosed herein and described above with reference to at least FIG. 2 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system for mobile flight simulator in an electric aircraft, thesystem comprising: a first simulator housing instrument, wherein thefirst simulator housing instrument is configured to house a plurality offirst flight simulator components; a second simulator housinginstrument, wherein the second simulator housing instrument isconfigured to house a plurality of second flight simulator components; apilot control, wherein the pilot control is configured to: receive apilot command; and transmit the pilot command to a computing device; acomputing device, wherein the computing device is configured to:communicatively connect each first flight simulator component of theplurality of first flight simulator components and each second flightsimulator component of the plurality of second flight simulatorcomponents; receive the pilot command from the pilot control; generate amobile flight simulation as a function of the pilot command, the mobileflight simulation including an electric aircraft model, virtuallyrepresenting an electric aircraft, the electric aircraft modelconfigured to: simulate a performance of the electric aircraft as afunction of the pilot command; generate at least an actual torque datumand a model torque datum, wherein the actual torque datum and the modeltorque datum are used to produce a torque percentage datum; and providea feedback datum, representative of the simulated performance of theelectric aircraft, to the plurality of first flight simulator componentsand the plurality of second flight simulator components as a function ofthe simulated performance of the electric aircraft; and update theelectric aircraft model as a function of the pilot command, the mobileflight simulation and the feedback datum.
 2. The system of claim 1,wherein the pilot control is communicatively connected to one or more ofthe first simulator housing instrument and the second simulator housing.3. The system of claim 1, wherein the feedback datum comprises a batteryperformance metric associated with the simulated performance of theelectric aircraft.
 4. The system of claim 1, wherein the pilot controlreceives the pilot command from a user.
 5. The system of claim 1,wherein the first housing instruments include a shipping container. 6.The system of claim 1, wherein one or more of the plurality of firstflight components and the plurality of second flight components displaya visual representation of the electric aircraft model.
 7. The system ofclaim 1, wherein the plurality of first flight simulator componentsincludes a plurality of adjacent displays.
 8. The system of claim 1,wherein the feedback datum comprises a user score.
 9. The system ofclaim 1, wherein the electric aircraft model includes an eVTOL model.10. The system of claim 1, wherein the pilot command is configured toidentify a torque of a flight component of the electric aircraft modelas a function of an input of a user.
 11. A method for mobile flightsimulator in an electric aircraft, the method comprising: housing, at afirst simulator housing instrument, a plurality of first flightsimulator components; housing, at a second simulator housing instrument,a plurality of second flight simulator components; transmitting, at apilot control a pilot command to a computing device; communicativelyconnecting, at the computing device, each first flight simulatorcomponent of the plurality of first flight simulator components and eachsecond flight simulator component of the plurality of second flightsimulator components; generating, at the computing device, a mobileflight simulation as a function of the pilot command, the mobile flightsimulation including an electric aircraft model, virtually representingan electric aircraft; simulating, at the electric aircraft model, aperformance of the electric aircraft as a function of the pilot command;generating, at the electric aircraft model, at least an actual torquedatum and a model torque datum, wherein the actual torque datum and themodel torque datum are used to produce a torque percentage datum; andproviding, at the electric aircraft model, a feedback datum,representative of the simulated performance of the electric aircraft, tothe plurality of first flight simulator components and the plurality ofsecond flight simulator components as a function of the simulatedperformance of the electric aircraft; and updating, at the computingdevice, the electric aircraft model as a function of the pilot command,the mobile flight simulation and the feedback datum.
 12. The method ofclaim 11, wherein the pilot control is communicatively connected to oneor more of the first simulator housing instrument and the secondsimulator housing instrument.
 13. The method of claim 11, wherein thefeedback datum comprises a battery performance metric associated withthe simulated performance of the electric aircraft.
 14. The method ofclaim 11, wherein the pilot control receives the pilot command from auser.
 15. The method of claim 11, wherein the first simulator housinginstrument includes a shipping container.
 16. The method of claim 11,wherein one or more of the plurality of first flight simulatorcomponents and the plurality of second flight components display avisual representation of the electric aircraft model.
 17. The method ofclaim 11, wherein the first plurality of flight simulator componentsincludes a plurality of adjacent displays.
 18. The method of claim 1,wherein the feedback datum comprises a user score.
 19. The method ofclaim 11, wherein the electric aircraft model includes an eVTOL model.20. The method of claim 11, wherein the pilot command is configured toidentify a torque of a flight component of the electric aircraft modelas a function of an input of a user.